6 citations · 17 across the 11 of their papers we have counts for
11 papers
GPS-Enhanced Tourist Mobility Modeling with Seasonal Spatial Priors and LLM-Based Activity Chain Generation
Yifan Liu, Yanling Sang, Xishun Liao +6
Tourist mobility poses a distinct challenge for urban transportation planning. Unlike resident commuting, tourist travel is largely non-routine, attraction driven, and highly sensi…
Uncertainty-Aware Trip Purpose Inference from GPS Trajectories via POI Semantic Zones and Pareto Calibration
Bo Yang, Haoxuan Ma, Yifan Liu +4
Large-scale GPS trajectory data offer rich observations of human mobility, yet assigning trip purposes to detected stops remains challenging due to the absence of individual-level…
Beyond 9-to-5: A Generative Model for Augmenting Mobility Data of Underrepresented Shift Workers
Haoxuan Ma, Xishun Liao, Yifan Liu +2
This paper addresses a critical gap in urban mobility modeling by focusing on shift workers, a population segment comprising 15-20% of the workforce in industrialized societies yet…
Next-Generation Travel Demand Modeling with a Generative Framework for Household Activity Coordination
Xishun Liao, Haoxuan Ma, Yifan Liu +4
Travel demand models are critical tools for planning, policy, and mobility system design. Traditional activity-based models (ABMs), although grounded in behavioral theories, often…
MobiVerse: Scaling Urban Mobility Simulation with Hybrid Lightweight Domain-Specific Generator and Large Language Models
Yifan Liu, Xishun Liao, Haoxuan Ma +3
Understanding and modeling human mobility patterns is crucial for effective transportation planning and urban development. Despite significant advances in mobility research, there…
Learning Universal Human Mobility Patterns with a Foundation Model for Cross-domain Data Fusion
Haoxuan Ma, Xishun Liao, Yifan Liu +4
Human mobility modeling is critical for urban planning and transportation management, yet existing approaches often lack the integration capabilities needed to handle diverse data…
Reconstructing Human Mobility Pattern: A Semi-Supervised Approach for Cross-Dataset Transfer Learning
Xishun Liao, Yifan Liu, Chenchen Kuai +5
Understanding human mobility patterns is crucial for urban planning, transportation management, and public health. This study tackles two primary challenges in the field: the relia…
Driving with Regulation: Trustworthy and Interpretable Decision-Making for Autonomous Driving with Retrieval-Augmented Reasoning
Tianhui Cai, Yifan Liu, Zewei Zhou +6
Understanding and adhering to traffic regulations is essential for autonomous vehicles to ensure safety and trustworthiness. However, traffic regulations are complex, context-depen…
Human Mobility Modeling with Household Coordination Activities under Limited Information via Retrieval-Augmented LLMs
Yifan Liu, Xishun Liao, Haoxuan Ma +3
Understanding human mobility patterns has long been a challenging task in transportation modeling. Due to the difficulties in obtaining high-quality training datasets across divers…
Deep Activity Model: A Generative Approach for Human Mobility Pattern Synthesis
Xishun Liao, Qinhua Jiang, Brian Yueshuai He +3
Human mobility plays a crucial role in transportation, urban planning, and public health, but current approaches face important limitations. Existing deep learning models tend to o…
Semantic Trajectory Data Mining with LLM-Informed POI Classification
Yifan Liu, Chenchen Kuai, Haoxuan Ma +3
Human travel trajectory mining is crucial for transportation systems, enhancing route optimization, traffic management, and the study of human travel patterns. Previous rule-based…